Output figures comparing M2 by predator (should match Morten’s outputs)
M2_2017 <- read_csv(here("KeyRunComparisons" , "NorthSeaSMS2017", "who_eats_whom_level1.csv"))
M2_2020 <- read_csv(here("KeyRunComparisons" , "NorthSeaSMS2020", "who_eats_whom_level1.csv"))
plist = lapply(split(M2_2017, M2_2017$Prey), function(d) {
ggplot(d, aes(Year, Part.M2, fill=Predator)) +
geom_bar(stat = "identity") +
facet_wrap(Prey~Prey.age) +
xlab("Year") +
ylab("M2") +
theme_tufte() +
theme(legend.position="bottom")
})
plist$Cod
plist$Haddock
plist$Herring
plist$'N. sandeel'
plist$'S. sandeel'
plist$'Nor. pout'
plist$Sprat
plist$Whiting
plist2 = lapply(split(M2_2020, M2_2020$Prey), function(d) {
ggplot(d, aes(Year, Part.M2, fill=Predator)) +
geom_bar(stat = "identity") +
facet_wrap(Prey~Prey.age) +
xlab("Year") +
ylab("M2") +
theme_tufte() +
theme(legend.position="bottom")
})
plist2$Cod
plist2$Haddock
plist2$Herring
plist2$'N. sandeel'
plist2$'S. sandeel'
plist2$'Nor. pout'
plist2$Sprat
plist2$Whiting
plist3 = lapply(split(M2_2017, M2_2017$Prey), function(d) {
ggplot(d, aes(Year, Part.M2, fill=Predator)) +
geom_bar(stat = "identity") +
facet_wrap(Prey~Prey.age, scales = "free_y") +
xlab("Year") +
ylab("M2") +
theme_tufte() +
theme(legend.position="bottom")
})
plist3$Cod
plist3$Haddock
plist3$Herring
plist3$'N. sandeel'
plist3$'S. sandeel'
plist3$'Nor. pout'
plist3$Sprat
plist3$Whiting
plist4 = lapply(split(M2_2020, M2_2020$Prey), function(d) {
ggplot(d, aes(Year, Part.M2, fill=Predator)) +
geom_bar(stat = "identity") +
facet_wrap(Prey~Prey.age, scales = "free_y") +
xlab("Year") +
ylab("M2") +
theme_tufte() +
theme(legend.position="bottom")
})
plist4$Cod
plist4$Haddock
plist4$Herring
plist4$'N. sandeel'
plist4$'S. sandeel'
plist4$'Nor. pout'
plist4$Sprat
plist4$Whiting
M2_2017 <- add_column(M2_2017, KeyRun = "SMS2017")
M2_2020 <- add_column(M2_2020, KeyRun = "SMS2020")
M2_comp <- bind_rows(M2_2017, M2_2020)
M2_compHerring <- M2_comp %>%
filter(Prey=="Herring") %>%
group_by(Year, Predator, Prey, Prey.age, KeyRun) %>%
summarise_at(vars(eatenW, Part.M2), funs(sum))
plist5 = lapply(split(M2_compHerring, M2_compHerring$Predator), function(d) {
ggplot(d, aes(Year, Part.M2, group=KeyRun)) +
geom_point(aes(colour=KeyRun)) +
facet_wrap(Prey~Prey.age, scales = "free_y", ncol = 2) +
xlab("Year") +
ylab("M2") +
theme_tufte() +
theme(legend.position="bottom")
})
plist5$Cod
plist5$Fulmar
plist5$'G. gurnards'
plist5$Gannet
plist5$'GBB. Gull'
plist5$'Grey seal'
plist5$Guillemot
plist5$'H. porpoise'
plist5$Hake
plist5$'Her. Gull'
plist5$Kittiwake
plist5$Mackerel
plist5$'N.horse mac'
plist5$Puffin
plist5$Razorbill
plist5$Saithe
plist5$Whiting
Find biggest M2 differences for age 2 herring between key-runs:
M2_diffHerring <- M2_compHerring %>%
arrange(Year, Predator, Prey, Prey.age, KeyRun) %>%
group_by(Year, Predator, Prey, Prey.age) %>%
mutate(diffM2_2020 = c(diff(Part.M2), NA),
diffeatenW_2020 = c(diff(eatenW), NA)) %>%
filter(!is.na(diffM2_2020))
age2diff <- M2_diffHerring %>%
filter(Prey.age==2) %>%
arrange(diffM2_2020)
library(DT)
datatable(age2diff, rownames = FALSE, options = list(pageLength = 25))